Profiling For Laziness.
Stephen Chang and Matthias Felleisen.
41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL 2014), San Diego, CA, USA, January 2014.

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While many programmers appreciate the benefits of lazy programming at an abstract level, determining which parts of a concrete program to evaluate lazily poses a significant challenge for most of them. Over the past thirty years, experts have published numerous papers on the problem, but developing this level of expertise requires a significant amount of experience.

We present a profiling-based technique that captures and automates this expertise for the insertion of laziness annotations into strict programs. To make this idea precise, we show how to equip a formal semantics with a metric that measures waste in an evaluation. Then we explain how to implement this metric as a dynamic profiling tool that suggests where to insert laziness into a program. Finally, we present evidence that our profiler's suggestions either match or improve on an expert's use of laziness in a range of real-world applications.